In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import ipywidgets as widgets
import plotly.express as px
from IPython.display import display
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.io as pio
# Load the data
data = pd.read_csv('filtered_US_aidpk.csv')

# Display the first few rows
data.head()
Out[1]:
Fiscal Year US Category Name US Sector Name Current Dollar Amount Activity Name Implementing Partner Category Name Implementing Partner Sub-category ID Implementing Partner Name
0 2023 Humanitarian Assistance Protection, Assistance and Solutions 25076538 Emergency Food Assistance Multilateral Multilateral - United Nations World Food Program
1 2023 Economic Development Agriculture 8000000 Emergency food assistance Multilateral Multilateral - United Nations Food and Agriculture Organisation
2 2023 Health Pandemic Influenza and Other Emerging Threats ... 6100451 Integrated Health Systems Strengthening and Se... NGO NGO - United States John Snow International
3 2023 Humanitarian Assistance Protection, Assistance and Solutions 5157625 Humanitarian Assistance - provide rapid respon... NGO NGO - United States NGO - United States Redacted
4 2023 Program Support Direct Administrative Costs 4044723 USAID Pay and Benefits Government Government - United States U.S. Government - U.S. Agency for Internationa...
In [2]:
# Create a sorted list of years
years = sorted(data['Fiscal Year'].unique())

# Create a dropdown widget
year_widget = widgets.Dropdown(
    options=years,
    value=years[0],
    description='Year:',
    disabled=False,
)

# Function to update the chart
def update_chart(year):
    # Filter data for selected year
    data_year = data[data['Fiscal Year'] == year]
    
    # Group by category and sum dollar amounts
    category_sums = data_year.groupby('US Category Name')['Current Dollar Amount'].sum()
    
    # Create bar chart
    plt.figure(figsize=(10, 6))
    plt.bar(category_sums.index, category_sums.values)
    plt.xlabel('Category')
    plt.ylabel('Total Dollar Amount')
    plt.title(f'Total Aid Amount by Category in {year}')
    plt.xticks(rotation=90)
    plt.show()

# Display the widget and chart
display(year_widget)
_ = widgets.interact(update_chart, year=year_widget)

Dropdown(description='Year:', options=(2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,…
interactive(children=(Dropdown(description='Year:', options=(2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2…
In [3]:
# Total aid given each year
fig1 = px.bar(data, x='Fiscal Year', y='Current Dollar Amount', 
             hover_data=['US Category Name'],  # Add this line
              labels={'Current Dollar Amount':'Dollar Amount (in billions $)',
                      'Fiscal Year': 'Year' , 'US Category Name': 'Ca'},
              title='Total US Aid Given Each Year 2000-2023',
              template='plotly_white')
fig1.update_traces(marker_color='skyblue')
fig1.update_layout(yaxis_tickprefix = '$', yaxis_ticksuffix = 'B')
fig1.show()

# Distribution of aid among different categories
data_category = data.groupby('US Category Name')['Current Dollar Amount'].sum().reset_index()
fig2 = px.pie(data_category, names='US Category Name', values='Current Dollar Amount', 
             labels={'US Category Name': 'Category'},
              title='Distribution of Aid Among Categories',
              template='plotly_white')
fig2.show()

# Top 10 sectors receiving aid
data_sector = data.groupby('US Sector Name')['Current Dollar Amount'].sum().nlargest(10).reset_index()
fig3 = px.bar(data_sector, y='US Sector Name', x='Current Dollar Amount', 
              labels={'Current Dollar Amount':'Total Dollar Amount (in billions)',
                      'US Sector Name': 'Sector'},
              title='Top 10 Sectors Receiving Aid',
              template='plotly_white', orientation='h')
fig3.update_traces(marker_color='skyblue')
fig3.update_layout(xaxis_tickprefix = '$', xaxis_ticksuffix = 'B')
fig3.show()
In [4]:
# Group the data by year and category, then reset the index
data_grouped = data.groupby(['Fiscal Year', 'US Category Name'])['Current Dollar Amount'].sum().reset_index()

# Create a list of unique years
years = data_grouped['Fiscal Year'].unique()

# Create a figure
fig4 = go.Figure()

# Add one trace for each year
for year in years:
    fig4.add_trace(
        go.Pie(
            labels=data_grouped[data_grouped['Fiscal Year'] == year]['US Category Name'],
            values=data_grouped[data_grouped['Fiscal Year'] == year]['Current Dollar Amount'] / 1e6,  # Convert to billions
            name=str(year),
            visible=False if year != min(years) else True,  # Only the first year is visible initially
            hoverinfo='label+value+percent',
            textinfo='label+value',
            texttemplate='%{label}<br>$%{value:.2f}mn',
            hole=0.7
        )
    )

# Add a dropdown menu to select the year
buttons = []
for i, year in enumerate(years):
    visibility = [False] * len(years)
    visibility[i] = True
    button = dict(
        label = str(year),
        method = 'update',
        args = [{'visible': visibility}]
    )
    buttons.append(button)

fig4.update_layout(

    margin=dict(
       
        t=130,  # top margin
        pad=100  # padding
    ),
 
    updatemenus=[
        dict(
            type="dropdown",
            direction="down",
            buttons=buttons,
            showactive=True,
            xanchor="right",
            yanchor="bottom",
            
        )
    ],
    title='US AID to Pakistan -Distribution of Aid Among Categories 2001-2023',
    template='plotly_white'
)

fig4.show()
In [6]:
from IPython.display import display, clear_output

# Create a year slider
year_slider = widgets.IntSlider(min=data['Fiscal Year'].min(), max=data['Fiscal Year'].max(), step=1, description='Year')

# Create output widgets for the figures
output_fig1 = widgets.Output()
output_fig2 = widgets.Output()
output_fig3 = widgets.Output()
output_fig4 = widgets.Output()

def display_figures(year):
    with output_fig1:
        clear_output(wait=True)  # Clear previous figure
        fig1.show()

    with output_fig2:
        clear_output(wait=True)  # Clear previous figure
        fig2.show()

    with output_fig3:
        clear_output(wait=True)  # Clear previous figure
        fig3.show()

    with output_fig4:
        clear_output(wait=True)  # Clear previous figure

        # Filter data for the selected year
        data_year = data[data['Fiscal Year'] == year]

        # Group the data by category, then reset the index
        data_grouped = data_year.groupby('US Category Name')['Current Dollar Amount'].sum().reset_index()

        # Create the pie chart
        fig = go.Figure(
            data=[go.Pie(
                labels=data_grouped['US Category Name'],
                values=data_grouped['Current Dollar Amount'] / 1e6,  # Convert to millions
                hoverinfo='label+value+percent',
                textinfo='label+value',
                texttemplate='%{label}<br>$%{value:.2f}B',
                hole=0.7
            )]
        )

        fig.update_layout(
            title='US Aid to Pakistan - Distribution of Aid Among Categories in {}'.format(year),
            template='plotly_white'
        )

        fig.show()

# Call the function with the minimum year to display the figures initially
display_figures(data['Fiscal Year'].min())

# Create a widget container to display the slider and the figures
widget_container = widgets.VBox([year_slider, output_fig1, output_fig2, output_fig3, output_fig4])

# Display the widget container
display(widget_container)

# Connect the slider to the function
year_slider.observe(lambda change: display_figures(change.new), 'value')

VBox(children=(IntSlider(value=2001, description='Year', max=2023, min=2001), Output(), Output(), Output(), Ou…
In [ ]: